Signal Processing and AI Applications

Abstract

Signal processing—the science of representing, analyzing, and transforming information-bearing signals—has become a cornerstone of modern engineering, enabling technologies from communications to biomedical imaging. Recent advances in artificial intelligence (AI) have transformed traditional signal processing by introducing data-driven methods that can learn complex patterns, detect anomalies, and make decisions with limited human supervision. This paper presents a comprehensive exploration of AI-augmented signal processing frameworks that integrate adaptive filtering, spectral feature extraction, and deep learning networks for real-time signal interpretation and classification. Applications across radar target detection, biomedical diagnostics, and communication systems are examined. The proposed hybrid methodology combines classical digital signal processing (DSP) techniques—such as Fast Fourier Transform (FFT) and wavelet analysis—with convolutional and recurrent neural networks, enabling automatic feature learning in noisy, non-stationary environments. Experiments using radar echo data, ECG signals, and acoustic communications demonstrate efficiency improvements of over 20% in classification accuracy and 35% reductions in response latency compared to conventional DSP pipelines. The findings highlight how AI-driven signal processing bridges statistical analysis and intelligent decision-making, offering high-performance solutions for next-generation sensing, communication, and medical platforms.